By Baltej Singh, Co-founder & CTO, SanchiConnect
Product–market fit has long been considered the defining milestone for any new idea—it signals when a product effectively solves a problem for its intended audience and gains momentum for growth. Traditionally, this was achieved by balancing functionality, price, and usability. But with the rise of DeepTech—covering advanced fields like artificial intelligence, biotechnology, robotics, and quantum computing—the path to product–market fit is becoming more nuanced. These technologies can transform industries and tackle some of humanity’s toughest challenges. While many DeepTech solutions face adoption challenges, this is not universal; breakthroughs like ChatGPT, DeepMind, and AlphaFold show that rapid adoption is possible when the technology aligns with real human needs. The key is designing and delivering innovations in ways that people can understand, trust, and use—marking a new frontier where human-centric design meets DeepTech.
The Limits of Technology-Led Thinking
DeepTech often emerges after years of research and development, sometimes requiring significant investment. These innovations can be technically brilliant but also intimidating, distancing them from everyday users. A common pitfall is creating solutions that impress experts but are difficult for the intended audience to understand or use. When the human factor is overlooked, even the most promising technology can struggle to gain traction.
Consider early-stage technologies designed for expert users, filled with jargon, steep learning curves, and complex interfaces. While fascinating to engineers, these features can alienate the people who would benefit most. Bridging this gap requires more than technical excellence—it demands empathy, observation, and design that prioritises real-world human contexts.
Why Human-Centric Design Matters
Human-centric design goes beyond aesthetics. It involves understanding users’ needs, limitations, and experiences and embedding these insights into the product. In DeepTech, this approach transforms complexity into clarity and builds trust in technologies that might otherwise feel overwhelming.
DeepTech presents unique challenges that make human-centric design more critical than ever. Three principles are particularly relevant:
Accessibility: Complex technologies must be simplified for diverse users through intuitive interfaces, clear explanations, and easy onboarding.
Relevance: Solutions must address real human or societal problems rather than pursuing novelty for its own sake.
Trust and Ethics: DeepTech often intersects with sensitive domains like data privacy, safety, and fairness. Ethical design ensures adoption is sustainable and responsible.
For example-
Healthcare: AI-based diagnostic tools can detect diseases earlier than doctors in controlled environments. But in hospitals, adoption lagged until designers replaced complex dashboards with simple, color-coded risk scores that doctors could quickly interpret during patient rounds. Once aligned with workflow, adoption soared.
Climate-Tech: A startup developing solar-powered irrigation pumps initially struggled in rural India because the system relied on smartphone apps in regions with patchy connectivity. Redesigning for offline controls and providing local-language training materials significantly increased adoption. The breakthrough wasn’t the pump—it was empathy baked into the solution.
These examples highlight that context matters as much as innovation itself.
From Adoption to Advocacy
When DeepTech solutions align with human-centric design, adoption can evolve into advocacy. Users empowered by a product become ambassadors, accelerating acceptance and growth. Technologies that demystify their functions and make users feel in control shift from “too advanced” to approachable and trustworthy, building loyalty and emotional resonance alongside functionality.
Designing for Real-World Contexts
Another critical layer is understanding the environment in which the technology will be used. Solutions interact with systems, communities, and cultures, and misalignment can hinder adoption. A healthcare innovation ignoring the emotional burden on patients or caregivers may fail despite technical sophistication. A rural technology designed without considering connectivity, affordability, or cultural norms may remain unused. Designing for real-world contexts ensures DeepTech integrates seamlessly into daily life.
The Shift in Product-Market Fit Metrics
Human-centric design also reshapes how we measure product–market fit. Beyond revenue, user numbers, or retention, new indicators include:
Ease of Use: Can users engage without extensive training?
Emotional Response: Does the product reduce fear, uncertainty, or frustration?
Ethical Acceptance: Are users comfortable with data handling and safety measures?
Cultural Compatibility: Does the product resonate with community values and norms?
By considering these qualitative measures, organisations can better evaluate whether innovations are truly aligned with human needs.
The Road Ahead
As DeepTech evolves, success will come not solely from superior algorithms or hardware but from the ability to make technology human. The new frontier of product–market fit lies in designing solutions that are as empathetic as they are advanced.
This does not mean simplifying or diluting innovation. It means translating complexity into clarity, embedding ethics into functionality, and putting users at the center of every decision. In doing so, DeepTech moves from labs to real-world impact, achieving both technological and human resonance.